Leveraging Single-Cell Approaches in Cancer Precision Medicine

Nath, Aritro; Bild, Andrea

doi:10.1016/j.trecan.2021.01.007

Cited by 22 publications

(13 citation statements)

References 136 publications

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“… 17 Valid targets for the cross entropy test include any high-dimensional single-cell dataset routinely generated by single-cell sequencing, flow cytometry, or mass cytometry in fields ranging from immunology 18 to neuroscience 19 to cancer. 20 The growth of single-cell technologies expands the potential range to techniques such as CODEX or other high-dimensional imaging technology, 21 single-cell proteomics, 22 or mutation analysis in single-cell sequencing. 23 More broadly, t-SNE and UMAP use can be implemented in cases where the cell is not the individual unit.…”

Section: Discussionmentioning

confidence: 99%

A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations

Roca

Burton

Neumann

et al. 2023

Cell Reports Methods

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Section: Discussionmentioning

confidence: 99%

A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations

Roca

Burton

Neumann

et al. 2023

Cell Reports Methods

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“…Single cell RNA-seq of tumor samples have proved indispensable for TME studies. This has allowed researchers to perform analyses such as in-depth classification of the composition of tumors, identifying the key signaling mechanisms operating in cancer and non-cancer cells and characterizing the heterogeneity and evolution of cancer cells, which were not previously feasible using bulk-RNA sequencing (Nath and Bild, 2021). However, the detection of rare cell populations among the diverse TME is limited by the number of cells the scRNA-seq platform can handle.…”

Section: Discussionmentioning

confidence: 99%

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types

et al. 2022

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Recent advances in single cell RNA sequencing (scRNA-seq) technologies have been invaluable in the study of the diversity of cancer cells and the tumor microenvironment. While scRNA-seq platforms allow processing of a high number of cells, uneven read quality and technical artifacts hinder the ability to identify and classify biologically relevant cells into correct subtypes. This obstructs the analysis of cancer and normal cell diversity, while rare and low expression cell populations may be lost by setting arbitrary high cutoffs for UMIs when filtering out low quality cells. To address these issues, we have developed a novel machine-learning framework that: 1. Trains cell lineage and subtype classifier using a gold standard dataset validated using marker genes 2. Systematically assess the lowest UMI threshold that can be used in a given dataset to accurately classify cells 3. Assign accurate cell lineage and subtype labels to the lower read depth cells recovered by setting the optimal threshold. We demonstrate the application of this framework in a well-curated scRNA-seq dataset of breast cancer patients and two external datasets. We show that the minimum UMI threshold for the breast cancer dataset could be lowered from the original 1500 to 450, thereby increasing the total number of recovered cells by 49%, while achieving a classification accuracy of >0.9. Our framework provides a roadmap for future scRNA-seq studies to determine optimal UMI threshold and accurately classify cells for downstream analyses.

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“…Single cell technology is prospective approach in field of cancer treatment and precision medicine which can provide a profile of heterogeneities in tumors and their environment and also novel therapeutic strategies against resistance tumors can be developed [ 151 ]. Due to different response of same tumors to specific drug and heterogeneity, it seems necessary to take advantages of single cell analysis [ 152 ].…”

Section: Advanced Strategies For Crc Drug Deliverymentioning

confidence: 99%

Recent advances in targeted drug delivery systems for resistant colorectal cancer

et al. 2022

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Colorectal cancer (CRC) is one of the deadliest cancers in the world, the incidences and morality rate are rising and poses an important threat to the public health. It is known that multiple drug resistance (MDR) is one of the major obstacles in CRC treatment. Tumor microenvironment plus genomic instability, tumor derived exosomes (TDE), cancer stem cells (CSCs), circulating tumor cells (CTCs), cell-free DNA (cfDNA), as well as cellular signaling pathways are important issues regarding resistance. Since non-targeted therapy causes toxicity, diverse side effects, and undesired efficacy, targeted therapy with contribution of various carriers has been developed to address the mentioned shortcomings. In this paper the underlying causes of MDR and then various targeting strategies including exosomes, liposomes, hydrogels, cell-based carriers and theranostics which are utilized to overcome therapeutic resistance will be described. We also discuss implication of emerging approaches involving single cell approaches and computer-aided drug delivery with high potential for meeting CRC medical needs.

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Leveraging Single-Cell Approaches in Cancer Precision Medicine

Cited by 22 publications

References 136 publications

A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations

A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations

A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types

Recent advances in targeted drug delivery systems for resistant colorectal cancer

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